A HapMap harvest of insights into the genetics of common disease

Kernel-machine testing coupled with a rank-truncation method for genetic pathway analysis

Traditional genome-wide association studies (GWASs) usually focus on single-marker analysis, which only accesses marginal effects. Pathway analysis, on the other hand, considers biological pathway gene marker hierarchical structure and therefore provides additional insights into the genetic architecture underlining complex diseases. Recently, a number of methods for pathway analysis have been proposed to assess the significance of a biological pathway from a collection of single-nucleotide polymorphisms. In this study, we propose a novel approach for pathway analysis that assesses the effects of genes using the sequence kernel association test and the effects of pathways using an extended adaptive rank truncated product statistic. It has been increasingly recognized that complex diseases are caused by both common and rare variants. We propose a new weighting scheme for genetic variants across the whole allelic frequency spectrum to be analyzed together without any form of frequency cutoff for defining rare variants. The proposed approach is flexible. It is applicable to both binary and continuous traits, and incorporating covariates is easy. Furthermore, it can be readily applied to GWAS data, exome-sequencing data, and deep resequencing data. We evaluate the new approach on data simulated under comprehensive scenarios and show that it has the highest power in most of the scenarios while maintaining the correct type I error rate. We also apply our proposed methodology to data from a study of the association between bipolar disorder and candidate pathways from Wellcome Trust Case Control Consortium (WTCCC) to show its utility.

Association between vascular endothelial growth factor gene polymorphisms and bladder cancer risk

Vascular endothelial growth factor (VEGF) gene polymorphisms are associated with susceptibility to a number of cancers. The present case-controlled study aimed to investigate the correlation between VEGF gene polymorphisms and the risk of bladder cancer. The effects of VEGF polymorphisms were investigated in patients with bladder cancer and healthy controls in our hospital between May, 2008 and May, 2013. Peripheral blood samples were obtained from 480 patients with bladder cancer and 420 healthy controls. The polymerase chain reaction-restriction fragment length polymorphism technique was used to detect three VEGF gene polymorphisms (rs3025039 C/T, rs833052 C/A and rs1570360 G/A) in these subjects. The genotype and allele frequencies were also investigated in order to determine their association with stage, grade and histological type of bladder cancer, as well as smoking status. Our results suggested that the frequency of the rs833052 AA genotype was significantly higher in patients with bladder cancer [odds ratio (OR)=1.75; 95% confidence interval (CI): 1.05-2.92; P=0.03] compared to that in healthy controls. There was no significant correlation between the rs833052 AA genotype and bladder cancer stage, grade or histological type, whereas smoking was identified as a risk factor for bladder cancer in the included patients (OR=1.48; 95% CI: 1.13-1.93; P=0.004). The rs3025039 and rs1570360 gene polymorphisms were not found to be correlated with the risk of bladder cancer or its progression. In conclusion, our results suggested that the VEGF rs833052 C/A polymorphism may be associated with a modest increase in the risk of bladder cancer in Chinese individuals.

Big data, open science and the brain: lessons learned from genomics

The BRAIN Initiative aims to break new ground in the scale and speed of data collection in neuroscience, requiring tools to handle data in the magnitude of yottabytes (10²⁴). The scale, investment and organization of it are being compared to the Human Genome Project (HGP), which has exemplified “big science” for biology. In line with the trend towards Big Data in genomic research, the promise of the BRAIN Initiative, as well as the European Human Brain Project, rests on the possibility to amass vast quantities of data to model the complex interactions between the brain and behavior and inform the diagnosis and prevention of neurological disorders and psychiatric disease. Advocates of this “data driven” paradigm in neuroscience argue that harnessing the large quantities of data generated across laboratories worldwide has numerous methodological, ethical and economic advantages, but it requires the neuroscience community to adopt a culture of data sharing and open access to benefit from them. In this article, we examine the rationale for data sharing among advocates and briefly exemplify these in terms of new “open neuroscience” projects. Then, drawing on the frequently invoked model of data sharing in genomics, we go on to demonstrate the complexities of data sharing, shedding light on the sociological and ethical challenges within the realms of institutions, researchers and participants, namely dilemmas around public/private interests in data, (lack of) motivation to share in the academic community, and potential loss of participant anonymity. Our paper serves to highlight some foreseeable tensions around data sharing relevant to the emergent “open neuroscience” movement.

Review article: genetic factors that modify the outcome of viral hepatitis

BACKGROUND

Genetic factors can play an important role for treatment response and disease progression in chronic viral hepatitis.

AIM

To review the influence of host genetic factors on the clinical course as well as on treatment response in patients with viral hepatitis.

METHODS

Review of the literature.

RESULTS

A landmark genome-wide association study (GWAS) identified polymorphisms in the IL28B gene on chromosome 19 (19q13.13) associated with response to therapy with pegylated interferon-α (PEG-IFN) and ribavirin (RBV) and spontaneous viral clearance in acute hepatitis C. Furthermore, IL28B genotype is associated with changes of lipid metabolism and insulin resistance. A further GWAS demonstrated that ITPA genetic variants protect HCV genotype 1 patients from RBV-induced anaemia. Another polymorphism in the patatin-like phospholipase domain containing 3 (PNPLA3) is associated with hepatic steatosis. Difficult-to-treat hepatitis C patients homozygous for GG had an up to five-fold lower chance of viral clearance on PEG/RBV than non-GG patients. In chronic hepatitis B patients treated with PEG-IFN several retrospective analyses of IL28B rs12980275 and rs12979860 genotypes yielded conflicting results which can be explained by the heterogeneity between the study populations. Some variants of the HLA-DP locus (HLA-DPA1 A allele and HLA-DPB1) protect against progression of chronic hepatitis B infection.

CONCLUSIONS

The determination of IL28B polymorphisms may be useful to individualise treatment options when using PEG/RBV based therapies for chronic hepatitis C infection. In contrast, so far identified genetic factors play only a minor role in chronic hepatitis B infection.

Combined analysis of exon splicing and genome wide polymorphism data predict schizophrenia risk loci

Schizophrenia has a strong genetic basis, and genome-wide association studies (GWAS) have shown that effect sizes for individual genetic variants which increase disease risk are small, making detection and validation of true disease-associated risk variants extremely challenging. Specifically, we first identify genes with exons showing differential expression between cases and controls, indicating a splicing mechanism that may contribute to variation in disease risk and focus on those showing consistent differential expression between blood and brain tissue. We then perform a genome-wide screen for SNPs associated with both normalised exon intensity of these genes (so called splicing QTLs) as well as association with schizophrenia. We identified a number of significantly associated loci with a biologically plausible role in schizophrenia, including MCPH1, DLG3, ZC3H13, and BICD2, and additional loci that influence splicing of these genes, including YWHAH. Our approach of integrating genome-wide exon intensity with genome-wide polymorphism data has identified a number of plausible SZ associated loci.

Canola oil influence on azoxymethane-induced colon carcinogenesis, hypertriglyceridemia and hyperglycemia in Kunming mice

Azoxymethane (AOM) is a potent genotoxic carcinogen which specifically induces colon cancer. Hyperlipidemia and diabetes have several influences on colon cancer development, with genetic and environmental exposure aspects. Here, we investigated plasma lipid and glucose concentrations in Kunming mice randomized into four groups; control (no AOM or oil exposure), AOM control, AOM + pork oil, and AOM + canola oil. Aberrant crypt foci (ACF), plasma cholesterol, plasma triglyceride, plasma glucose and organ weight were examined 32 weeks after AOM injection. Results revealed that AOM exposure significantly increased ACF number, plasma triglyceride and glucose level. Further, male mice displayed a much higher plasma triglyceride level than female mice in the AOM control group. Dietary fat significantly inhibited AOM-induced hypertriglyceridemia, and canola oil had stronger inhibitory effect than pork oil. AOM-induced hyperglycemia had no sex-difference and was not significantly modified by dietary fat. However, AOM itself not change plasma cholesterol level. AOM significantly increased liver and spleen weight in male mice, but decreased kidney weight in female mice. On the other hand, mice testis weight decreased when fed canola oil. AOM could induce colorectal carcinogenesis, hypertriglyceridemia and hyperglycemia in Kunming mice at the same time, with subsequent studies required to investigate their genome association.

Recent progress in omics-driven analysis of MS to unravel pathological mechanisms

At present, the pathophysiology and specific biological markers reflecting pathology of multiple sclerosis (MS) remain undetermined. The risk of developing MS is considered to depend on genetic susceptibility and environmental factors. The interaction of environmental factors with epigenetic mechanisms could affect the transcriptional level and therefore also the translational level. In the last decade, growing amount of hypothesis-free 'omics' studies have shed light on the potential MS mechanisms and raised potential biomarker targets. To understand MS pathophysiology and discover a subset of biomarkers, it is becoming essential to take a step forward and integrate the findings of the different fields of 'omics' into a systems biology network. In this review, we will discuss the recent findings of the genomic, transcriptomic and proteomic fields for MS and aim to make a unifying model.

Cell therapies and regenerative medicine

Molecular and cell biology has resulted in major advances in our understanding of disease pathogenesis as well as in novel strategies for the diagnosis, therapy and prevention of human diseases. Based on modern molecular, genetic and biochemical methodologies, it is on the one hand possible to identify disease-related point mutations and single nucleotide polymorphisms, for example. On the other hand, using high throughput array and other technologies, it is for example possible to simultaneously analyze thousands of genes or gene products (RNA and proteins), resulting in an individual gene or gene expression profile ('signature'). Such data increasingly allow defining the individual disposition for a given disease and predicting disease prognosis as well as the efficacy of therapeutic strategies in the individual patient ('personalized medicine'). At the same time, the basic discoveries in cell biology, including embryonic and adult stem cells, induced pluripotent stem cells, genetically modified cells and others, have moved regenerative medicine into the center of biomedical research worldwide with a major translational impact on tissue engineering as well as transplantation medicine. All these aspects have greatly contributed to the recent advances in regenerative medicine and the development of novel concepts for the treatment of many human diseases, including liver diseases.

Pediatric nephrolithiasis and the link to bone metabolism

PURPOSE OF REVIEW

To review the recent publications describing the link between pediatric nephrolithiasis and bone metabolism.

RECENT FINDINGS

Nephrolithiasis incidence is increasing in children and is associated with low bone mineral density (BMD). Affected children are conceptually at risk for fractures and osteoporosis. In addition to abnormal calcium metabolism, inflammation, genetic makeup and dietary habits are being recognized as important factors in the pathophysiology of nephrolithiasis and low bone density. Findings from retrospective reviews suggest that low BMD in children may be improved with citrate or thiazide treatment.

SUMMARY

The healthcare burden from low BMD with subsequent osteoporosis and fracture risk is immense with potential far-reaching effects in patient quality of life and healthcare expense. Bone mass is acquired in the pediatric age range, thus it is important to identify and treat at-risk children. Retrospective reviews in pediatric patients indicate that citrate or thiazide diuretic treatment may improve BMD. We now understand that a relationship exists between nephrolithiasis and low BMD. To improve healthcare for our current patients as well as protect their future health it is important to identify low BMD and initiate strategies to improve BMD in 'at-risk' children.

Identifying genetic relatives without compromising privacy

The development of high-throughput genomic technologies has impacted many areas of genetic research. While many applications of these technologies focus on the discovery of genes involved in disease from population samples, applications of genomic technologies to an individual's genome or personal genomics have recently gained much interest. One such application is the identification of relatives from genetic data. In this application, genetic information from a set of individuals is collected in a database, and each pair of individuals is compared in order to identify genetic relatives. An inherent issue that arises in the identification of relatives is privacy. In this article, we propose a method for identifying genetic relatives without compromising privacy by taking advantage of novel cryptographic techniques customized for secure and private comparison of genetic information. We demonstrate the utility of these techniques by allowing a pair of individuals to discover whether or not they are related without compromising their genetic information or revealing it to a third party. The idea is that individuals only share enough special-purpose cryptographically protected information with each other to identify whether or not they are relatives, but not enough to expose any information about their genomes. We show in HapMap and 1000 Genomes data that our method can recover first- and second-order genetic relationships and, through simulations, show that our method can identify relationships as distant as third cousins while preserving privacy.

Genetic factors regulating lung vasculature and immune cell functions associate with resistance to pneumococcal infection

Streptococcus pneumoniae is an important human pathogen responsible for high mortality and morbidity worldwide. The susceptibility to pneumococcal infections is controlled by as yet unknown genetic factors. To elucidate these factors could help to develop new medical treatments and tools to identify those most at risk. In recent years genome wide association studies (GWAS) in mice and humans have proved successful in identification of causal genes involved in many complex diseases for example diabetes, systemic lupus or cholesterol metabolism. In this study a GWAS approach was used to map genetic loci associated with susceptibility to pneumococcal infection in 26 inbred mouse strains. As a result four candidate QTLs were identified on chromosomes 7, 13, 18 and 19. Interestingly, the QTL on chromosome 7 was located within S. pneumoniae resistance QTL (Spir1) identified previously in a linkage study of BALB/cOlaHsd and CBA/CaOlaHsd F2 intercrosses. We showed that only a limited number of genes encoded within the QTLs carried phenotype-associated polymorphisms (22 genes out of several hundred located within the QTLs). These candidate genes are known to regulate TGFβ signalling, smooth muscle and immune cells functions. Interestingly, our pulmonary histopathology and gene expression data demonstrated, lung vasculature plays an important role in resistance to pneumococcal infection. Therefore we concluded that the cumulative effect of these candidate genes on vasculature and immune cells functions as contributory factors in the observed differences in susceptibility to pneumococcal infection. We also propose that TGFβ-mediated regulation of fibroblast differentiation plays an important role in development of invasive pneumococcal disease. Gene expression data submitted to the NCBI Gene Expression Omnibus Accession No: GSE49533

SNP data submitted to NCBI dbSNP Short Genetic Variation http://www.ncbi.nlm.nih.gov/projects/SNP/snp_viewTable.cgi?handle=MUSPNEUMONIA.

Advances in individualized and regenerative medicine

Molecular and cell biology have resulted in major advances in our understanding of disease pathogenesis as well as in novel strategies for the diagnosis, therapy and prevention of human diseases. Based on modern molecular, genetic and biochemical methodologies it is on the one hand possible to identify for example disease-related point mutations and single nucleotide polymorphisms. On the other hand, using high throughput array and other technologies, it is for example possible to simultaneously analyze thousands of genes or gene products (RNA and proteins), resulting in an individual gene or gene expression profile ('signature'). Such data increasingly allow to define the individual disposition for a given disease and to predict disease prognosis as well as the efficacy of therapeutic strategies in the individual patient ('individualized medicine'). At the same time, the basic discoveries in cell biology, including embryonic and adult stem cells, induced pluripotent stem cells, genetically modified cells and others, have moved regenerative medicine into the center of biomedical research worldwide with a major translational impact on tissue engineering as well as transplantation medicine. All these aspects have greatly contributed to the recent advances in regenerative medicine and the development novel concepts for the treatment of many human diseases, including liver diseases.

A statistical framework to guide sequencing choices in pedigrees

The use of large pedigrees is an effective design for identifying rare functional variants affecting heritable traits. Cost-effective studies using sequence data can be achieved via pedigree-based genotype imputation in which some subjects are sequenced and missing genotypes are inferred on the remaining subjects. Because of high cost, it is important to carefully prioritize subjects for sequencing. Here, we introduce a statistical framework that enables systematic comparison among subject-selection choices for sequencing. We introduce a metric "local coverage," which allows the use of inferred inheritance vectors to measure genotype-imputation ability specifically in a region of interest, such as one with prior evidence of linkage. In the absence of linkage information, we can instead use a "genome-wide coverage" metric computed with the pedigree structure. These metrics enable the development of a method that identifies efficient selection choices for sequencing. As implemented in GIGI-Pick, this method also flexibly allows initial manual selection of subjects and optimizes selections within the constraint that only some subjects might be available for sequencing. In the present study, we used simulations to compare GIGI-Pick with PRIMUS, ExomePicks, and common ad hoc methods of selecting subjects. In genotype imputation of both common and rare alleles, GIGI-Pick substantially outperformed all other methods considered and had the added advantage of incorporating prior linkage information. We also used a real pedigree to demonstrate the utility of our approach in identifying causal mutations. Our work enables prioritization of subjects for sequencing to facilitate dissection of the genetic basis of heritable traits.

Diagnostic exome sequencing: a new paradigm in neurology

Genomic tools have evolved with remarkable rapidity, but their clinical relevance and application have lagged behind. Now, consistent clinical applications have finally arrived and bring with them the promise of identifying the underlying causes of complex neurological disorders in a patient-specific manner.

Testing gene-gene interactions in genome wide association studies

Detection of gene-gene interaction has become increasingly popular over the past decade in genome wide association studies (GWAS). Besides traditional logistic regression analysis for detecting interactions between two markers, new methods have been developed in recent years such as comparing linkage disequilibrium (LD) in case and control groups. All these methods form the building blocks of most screening strategies for disease susceptibility loci in GWAS. In this paper, we are interested in comparing the competing methods and providing practical guidelines for selecting appropriate testing methods for interaction in GWAS. We first review a series of existing statistical methods to detect interactions, and then examine different definitions of interactions to gain insight into the theoretical relationship between the existing testing methods. Lastly, we perform extensive simulations to compare powers of various methods to detect either interaction between two markers at two unlinked loci or the overall association allowing for both interaction and main effects. This investigation reveals informative characteristics of various methods that are helpful to GWAS investigators.

Translational bioinformatics approaches for systems and dynamical medicine

The exponential growth of experimental and clinical data generated from systematic studies, the complexity in health and diseases, and the request for the establishment of systems models are bringing bioinformatics to the center stage of pharmacogenomics and systems biology. Bioinformatics plays an essential role in bridging the gap among different knowledge domains for the translation of the voluminous data into better diagnosis, prognosis, prevention, and treatment. Bioinformatics is essential in finding the spatiotemporal patterns in pharmacogenomics, including the time-series analyses of the associations between genetic structural variations and functional alterations such as drug responses. The elucidation of the cross talks among different systems levels and time scales can contribute to the discovery of accurate and robust biomarkers at various diseases stages for the development of systems and dynamical medicine. Various resources are available for such purposes, including databases and tools supporting "omics" studies such as genomics, proteomics, epigenomics, transcriptomics, metabolomics, lipidomics, pharmacogenomics, and chronomics. The combination of bioinformatics and health informatics methods would provide powerful decision support in both scientific and clinical environments. Data integration, data mining, and knowledge discovery (KD) methods would enable the simulation of complex systems and dynamical networks to establish predictive models for achieving predictive, preventive, and personalized medicine.

The road from next-generation sequencing to personalized medicine

Moving from a traditional medical model of treating pathologies to an individualized predictive and preventive model of personalized medicine promises to reduce the healthcare cost on an overburdened and overwhelmed system. Next-generation sequencing (NGS) has the potential to accelerate the early detection of disorders and the identification of pharmacogenetics markers to customize treatments. This review explains the historical facts that led to the development of NGS along with the strengths and weakness of NGS, with a special emphasis on the analytical aspects used to process NGS data. There are solutions to all the steps necessary for performing NGS in the clinical context where the majority of them are very efficient, but there are some crucial steps in the process that need immediate attention.

Guilt by rewiring: gene prioritization through network rewiring in genome wide association studies

Although Genome Wide Association Studies (GWAS) have identified many susceptibility loci for common diseases, they only explain a small portion of heritability. It is challenging to identify the remaining disease loci because their association signals are likely weak and difficult to identify among millions of candidates. One potentially useful direction to increase statistical power is to incorporate functional genomics information, especially gene expression networks, to prioritize GWAS signals. Most current methods utilizing network information to prioritize disease genes are based on the 'guilt by association' principle, in which networks are treated as static, and disease-associated genes are assumed to locate closer with each other than random pairs in the network. In contrast, we propose a novel 'guilt by rewiring' principle. Studying the dynamics of gene networks between controls and patients, this principle assumes that disease genes more likely undergo rewiring in patients, whereas most of the network remains unaffected in disease condition. To demonstrate this principle, we consider the changes of co-expression networks in Crohn's disease patients and controls, and how network dynamics reveals information on disease associations. Our results demonstrate that network rewiring is abundant in the immune system, and disease-associated genes are more likely to be rewired in patients. To integrate this network rewiring feature and GWAS signals, we propose to use the Markov random field framework to integrate network information to prioritize genes. Applications in Crohn's disease and Parkinson's disease show that this framework leads to more replicable results, and implicates potentially disease-associated pathways.

Genome wide association studies in presence of misclassified binary responses

BACKGROUND

Misclassification has been shown to have a high prevalence in binary responses in both livestock and human populations. Leaving these errors uncorrected before analyses will have a negative impact on the overall goal of genome-wide association studies (GWAS) including reducing predictive power. A liability threshold model that contemplates misclassification was developed to assess the effects of mis-diagnostic errors on GWAS. Four simulated scenarios of case-control datasets were generated. Each dataset consisted of 2000 individuals and was analyzed with varying odds ratios of the influential SNPs and misclassification rates of 5% and 10%.

RESULTS

Analyses of binary responses subject to misclassification resulted in underestimation of influential SNPs and failed to estimate the true magnitude and direction of the effects. Once the misclassification algorithm was applied there was a 12% to 29% increase in accuracy, and a substantial reduction in bias. The proposed method was able to capture the majority of the most significant SNPs that were not identified in the analysis of the misclassified data. In fact, in one of the simulation scenarios, 33% of the influential SNPs were not identified using the misclassified data, compared with the analysis using the data without misclassification. However, using the proposed method, only 13% were not identified. Furthermore, the proposed method was able to identify with high probability a large portion of the truly misclassified observations.

CONCLUSIONS

The proposed model provides a statistical tool to correct or at least attenuate the negative effects of misclassified binary responses in GWAS. Across different levels of misclassification probability as well as odds ratios of significant SNPs, the model proved to be robust. In fact, SNP effects, and misclassification probability were accurately estimated and the truly misclassified observations were identified with high probabilities compared to non-misclassified responses. This study was limited to situations where the misclassification probability was assumed to be the same in cases and controls which is not always the case based on real human disease data. Thus, it is of interest to evaluate the performance of the proposed model in that situation which is the current focus of our research.

On the simultaneous association analysis of large genomic regions: a massive multi-locus association test

MOTIVATION

For samples of unrelated individuals, we propose a general analysis framework in which hundred thousands of genetic loci can be tested simultaneously for association with complex phenotypes. The approach is built on spatial-clustering methodology, assuming that genetic loci that are associated with the target phenotype cluster in certain genomic regions. In contrast to standard methodology for multilocus analysis, which has focused on the dimension reduction of the data, our multilocus association-clustering test profits from the availability of large numbers of genetic loci by detecting clusters of loci that are associated with the phenotype.

RESULTS

The approach is computationally fast and powerful, enabling the simultaneous association testing of large genomic regions. Even the entire genome or certain chromosomes can be tested simultaneously. Using simulation studies, the properties of the approach are evaluated. In an application to a genome-wide association study for chronic obstructive pulmonary disease, we illustrate the practical relevance of the proposed method by simultaneously testing all genotyped loci of the genome-wide association study and by testing each chromosome individually. Our findings suggest that statistical methodology that incorporates spatial-clustering information will be especially useful in whole-genome sequencing studies in which millions or billions of base pairs are recorded and grouped by genomic regions or genes, and are tested jointly for association.

AVAILABILITY AND IMPLEMENTATION

Implementation of the approach is available upon request.

BDgene: a genetic database for bipolar disorder and its overlap with schizophrenia and major depressive disorder

BACKGROUND

Bipolar disorder (BD) is a common psychiatric disorder with complex genetic architecture. It shares overlapping genetic influences with schizophrenia (SZ) and major depressive disorder (MDD). Large numbers of genetic studies of BD and cross-disorder studies between BD and SZ/MDD have accumulated numerous genetic data. There is a growing need to integrate the data to provide a comprehensive data set to facilitate the genetic study of BD and its highly relevant diseases.

METHODS

BDgene database was developed to integrate BD-related genetic factors and shared ones with SZ/MDD from profound literature reading. On the basis of data from the literature, in-depth analyses were performed for further understanding of the data, including gene prioritization, pathway-based analysis, intersection analysis of multidisease candidate genes, and pathway enrichment analysis.

RESULTS

BDgene includes multiple types of literature-reported genetic factors of BD with both positive and negative results, including 797 genes, 3119 single nucleotide polymorphisms, and 789 regions. Shared genetic factors such as single nucleotide polymorphisms, genes, and regions from published cross-disorder studies among BD and SZ/MDD were also presented. In-depth data analyses identified 43 BD core genes; 70 BD candidate pathways; and 127, 79, and 107 new potential cross-disorder genes for BD-SZ, BD-MDD, and BD-SZ-MDD, respectively.

CONCLUSIONS

As a central genetic database for BD and the first cross-disorder database for BD and SZ/MDD, BDgene provides not only a comprehensive review of current genetic research but also high-confidence candidate genes and pathways for understanding of BD mechanism and shared etiology among its relevant diseases. BDgene is freely available at http://bdgene.psych.ac.cn.

An application and empirical comparison of statistical analysis methods for associating rare variants to a complex phenotype

The contribution of collections of rare sequence variations (or 'variants') to phenotypic expression has begun to receive considerable attention within the biomedical research community. However, the best way to capture the effects of rare variants in relevant statistical analysis models is an open question. In this paper we describe the application of a number of statistical methods for testing associations between rare variants in two genes to obesity. We consider the relative merits of the different methods as well as important implementation details, such as the leveraging of genomic annotations and determining p-values.

Genetic epidemiology of type 2 diabetes and cardiovascular diseases in Africa

The burdens of type 2 diabetes (T2D) and cardiovascular diseases (CVD) are increasing in Africa. T2D and CVD are the result of the complex interaction between inherited characteristics, lifestyle, and environmental factors. The epidemic of obesity is largely behind the exploding global incidence of T2D. However, not all obese individuals develop diabetes and positive family history is a powerful risk factor for diabetes and CVD. Recent implementations of high throughput genotyping and sequencing approaches have advanced our understanding of the genetic basis of diabetes and CVD by identifying several genomic loci that were not previously linked to the pathobiology of these diseases. However, African populations have not been adequately represented in these global genomic efforts. Here, we summarize the state of knowledge of the genetic epidemiology of T2D and CVD in Africa and highlight new genomic initiatives that promise to inform disease etiology, public health and clinical medicine in Africa.

The genetics of NAFLD

NAFLD is a disease spectrum ranging from simple steatosis, through steatohepatitis to fibrosis and, ultimately, cirrhosis. This condition is characterized by considerable interpatient variability in terms of severity and rate of progression: although a substantial proportion of the population is at risk of progressive disease, only a minority experience associated morbidity. As such, NAFLD is best considered a complex disease trait resulting from environmental exposures acting on a susceptible polygenic background and comprising multiple independent modifiers. Much ongoing research is focused on identifying the genetic factors that contribute to NAFLD pathogenesis. This Review describes the current status of the field, discussing specific genetic and epigenetic modifiers, including the mechanisms through which genes identified by genome-wide association studies, including PNPLA3, influence disease progression.

Genome-wide association studies of maximum number of drinks

Maximum number of drinks (MaxDrinks) defined as "Maximum number of alcoholic drinks consumed in a 24-h period" is an intermediate phenotype that is closely related to alcohol dependence (AD). Family, twin and adoption studies have shown that the heritability of MaxDrinks is approximately 0.5. We conducted the first genome-wide association (GWA) study and meta-analysis of MaxDrinks as a continuous phenotype. 1059 individuals were from the Collaborative Study on the Genetics of Alcoholism (COGA) sample and 1628 individuals were from the Study of Addiction - Genetics and Environment (SAGE) sample. Family sample with 3137 individuals was from the Australian twin-family study of alcohol use disorder (OZALC). Two population-based Caucasian samples (COGA and SAGE) with 1 million single-nucleotide polymorphisms (SNPs) were used for gene discovery and one family-based Caucasian sample was used for replication. Through meta-analysis we identified 162 SNPs associated with MaxDirnks (p < 10(-4)). The most significant association with MaxDrinks was observed with SNP rs11128951 (p = 4.27 × 10(-8)) near SGOL1 gene at 3p24.3. Furthermore, several SNPs (rs17144687 near DTWD2, rs12108602 near NDST4, and rs2128158 in KCNB2) showed significant associations with MaxDrinks (p < 5 × 10(-7)) in the meta-analysis. Especially, 8 SNPs in DDC gene showed significant associations with MaxDrinks (p < 5 × 10(-7)) in the SAGE sample. Several flanking SNPs in above genes/regions were confirmed in the OZALC family sample. In conclusions, we identified several genes/regions associated with MaxDrinks. These findings can improve the understanding about the pathogenesis of alcohol consumption phenotypes and alcohol-related disorders.

Human genome-guided identification of memory-modulating drugs

In the last decade there has been an exponential increase in knowledge about the genetic basis of complex human traits, including neuropsychiatric disorders. It is not clear, however, to what extent this knowledge can be used as a starting point for drug identification, one of the central hopes of the human genome project. The aim of the present study was to identify memory-modulating compounds through the use of human genetic information. We performed a multinational collaborative study, which included assessment of aversive memory--a trait central to posttraumatic stress disorder--and a gene-set analysis in healthy individuals. We identified 20 potential drug target genes in two genomewide-corrected gene sets: the neuroactive ligand-receptor interaction and the long-term depression gene set. In a subsequent double-blind, placebo-controlled study in healthy volunteers, we aimed at providing a proof of concept for the genome-guided identification of memory modulating compounds. Pharmacological intervention at the neuroactive ligand-receptor interaction gene set led to significant reduction of aversive memory. The findings demonstrate that genome information, along with appropriate data mining methodology, can be used as a starting point for the identification of memory-modulating compounds.

eALPS: estimating abundance levels in pooled sequencing using available genotyping data

The recent advances in high-throughput sequencing technologies bring the potential of a better characterization of the genetic variation in humans and other organisms. In many occasions, either by design or by necessity, the sequencing procedure is performed on a pool of DNA samples with different abundances, where the abundance of each sample is unknown. Such a scenario is naturally occurring in the case of metagenomics analysis where a pool of bacteria is sequenced, or in the case of population studies involving DNA pools by design. Particularly, various pooling designs were recently suggested that can identify carriers of rare alleles in large cohorts, dramatically reducing the cost of such large-scale sequencing projects. A fundamental problem with such approaches for population studies is that the uncertainty of DNA proportions from different individuals in the pools might lead to spurious associations. Fortunately, it is often the case that the genotype data of at least some of the individuals in the pool is known. Here, we propose a method (eALPS) that uses the genotype data in conjunction with the pooled sequence data in order to accurately estimate the proportions of the samples in the pool, even in cases where not all individuals in the pool were genotyped (eALPS-LD). Using real data from a sequencing pooling study of non-Hodgkin's lymphoma, we demonstrate that the estimation of the proportions is crucial, since otherwise there is a risk for false discoveries. Additionally, we demonstrate that our approach is also applicable to the problem of quantification of species in metagenomics samples (eALPS-BCR) and is particularly suitable for metagenomic quantification of closely related species.

Common dysfunctional variants in ABCG2 are a major cause of early-onset gout

Gout is a common disease which mostly occurs after middle age, but more people nowadays develop it before the age of thirty. We investigated whether common dysfunction of ABCG2, a high-capacity urate transporter which regulates serum uric acid levels, causes early-onset gout. 705 Japanese male gout cases with onset age data and 1,887 male controls were genotyped, and the ABCG2 functions which are estimated by its genotype combination were determined. The onset age was 6.5 years earlier with severe ABCG2 dysfunction than with normal ABCG2 function (P = 6.14 × 10(-3)). Patients with mild to severe ABCG2 dysfunction accounted for 88.2% of early-onset cases (twenties or younger). Severe ABCG2 dysfunction particularly increased the risk of early-onset gout (odds ratio 22.2, P = 4.66 × 10(-6)). Our finding that common dysfunction of ABCG2 is a major cause of early-onset gout will serve to improve earlier prevention and therapy for high-risk individuals.

Taiwan Schizophrenia Linkage Study: lessons learned from endophenotype-based genome-wide linkage scans and perspective

Taiwan Schizophrenia Linkage Study (TSLS) was initiated with a linkage strategy for locating multiple genes, each of small to moderate effect, and aimed to recruit a large enough sample of pairs of affected siblings and their families ascertained from a multisite study. With a sample of 607 families successfully recruited, a total of 2,242 individuals (1,207 affected and 1,035 unaffected) from 557 families were genotyped using 386 microsatellite markers spaced at an average of 9-cM intervals. Here the author reviews the establishment of TSLS and initial signal derived from linkage scan using the diagnosis of schizophrenia. Based on the limited success of the initial linkage analysis, a sufficient-component causal model is proposed to incorporate endophenotypes and genes for schizophrenia. Four types of candidate endophenotype measured in TSLS, including schizotypal personality, Continuous Performance Test, Wisconsin Card Sorting Test, and niacin skin flush test, are briefly described. The author discusses different strategies of linkage analysis incorporating these endophenotypes, including quantitative trait loci (QTL) linkage analysis, clustering-derived subgroups, ordered subset analysis (OSA), and latent classes for linkage scan. Then the author summarizes the linkage signals generated from seven studies of endophenotype-based linkage analysis using TSLS, including QTL scan of neurocognitive performance, QTL scan of niacin skin flush, the family cluster of attention deficit and execution deficit, OSA by schizophrenia-schizotypy factors, nested OSA by age at onset and neurocognitive performance, and the latent class of deficit schizophrenia for linkage analysis. The perspective of combining next-generation sequencing with linkage analysis of families is also discussed.

miRNAs and long noncoding RNAs as biomarkers in human diseases

Noncoding RNAs (ncRNAs) are transcripts that have no apparent protein-coding capacity; however, many ncRNAs have been found to play a major biological role in human physiology. Their deregulation is implicated in many human diseases, but their exact roles are only beginning to be elucidated. Nevertheless, ncRNAs are extensively studied as a novel source of biomarkers, and the fact that they can be detected in body fluids makes them extremely suitable for this purpose. The authors mainly focus on ncRNAs as biomarkers in cancer, but also touch on other human diseases such as cardiovascular diseases, autoimmune diseases, neurological disorders and infectious diseases. The authors discuss the established methods and provide a selection of emerging new techniques that can be used to detect and quantify ncRNAs. Finally, the authors discuss ncRNAs as a new strategy for therapeutic interventions.

Cis-acting regulation of brain-specific ANK3 gene expression by a genetic variant associated with bipolar disorder

Several genome-wide association studies for bipolar disorder (BD) have found a strong association of the Ankyrin 3 (ANK3) gene. This association spans numerous linked single-nucleotide polymorphisms (SNPs) in an ~250-kb genomic region overlapping ANK3. The associated region encompasses predicted regulatory elements as well as two of the six validated alternative first exons, which encode distinct protein domains at the N-terminus of the protein also known as Ankyrin-G. Using RNA ligase-mediated rapid amplification of cDNA ends to identify novel transcripts in conjunction with a highly sensitive, exon-specific multiplexed mRNA expression assay, we detected differential regulation of distinct ANK3 transcription start sites and coupling of specific 5' ends with 3' mRNA splicing events in postmortem human brain and human stem cell-derived neural progenitors and neurons. Furthermore, allelic variation at the BD-associated SNP rs1938526 correlated with a significant difference in cerebellar expression of a brain-specific ANK3 transcript. These findings suggest a brain-specific cis-regulatory transcriptional effect of ANK3 that may be relevant to BD pathophysiology.

Fast association tests for genes with FAST

Gene-based tests of association can increase the power of a genome-wide association study by aggregating multiple independent effects across a gene or locus into a single stronger signal. Recent gene-based tests have distinct approaches to selecting which variants to aggregate within a locus, modeling the effects of linkage disequilibrium, representing fractional allele counts from imputation, and managing permutation tests for p-values. Implementing these tests in a single, efficient framework has great practical value. Fast ASsociation Tests (Fast) addresses this need by implementing leading gene-based association tests together with conventional SNP-based univariate tests and providing a consolidated, easily interpreted report. Fast scales readily to genome-wide SNP data with millions of SNPs and tens of thousands of individuals, provides implementations that are orders of magnitude faster than original literature reports, and provides a unified framework for performing several gene based association tests concurrently and efficiently on the same data.

AVAILABILITY

https://bitbucket.org/baderlab/fast/downloads/FAST.tar.gz, with documentation at https://bitbucket.org/baderlab/fast/wiki/Home.

GStream: improving SNP and CNV coverage on genome-wide association studies

We present GStream, a method that combines genome-wide SNP and CNV genotyping in the Illumina microarray platform with unprecedented accuracy. This new method outperforms previous well-established SNP genotyping software. More importantly, the CNV calling algorithm of GStream dramatically improves the results obtained by previous state-of-the-art methods and yields an accuracy that is close to that obtained by purely CNV-oriented technologies like Comparative Genomic Hybridization (CGH). We demonstrate the superior performance of GStream using microarray data generated from HapMap samples. Using the reference CNV calls generated by the 1000 Genomes Project (1KGP) and well-known studies on whole genome CNV characterization based either on CGH or genotyping microarray technologies, we show that GStream can increase the number of reliably detected variants up to 25% compared to previously developed methods. Furthermore, the increased genome coverage provided by GStream allows the discovery of CNVs in close linkage disequilibrium with SNPs, previously associated with disease risk in published Genome-Wide Association Studies (GWAS). These results could provide important insights into the biological mechanism underlying the detected disease risk association. With GStream, large-scale GWAS will not only benefit from the combined genotyping of SNPs and CNVs at an unprecedented accuracy, but will also take advantage of the computational efficiency of the method.

The geometric increase in meta-analyses from China in the genomic era

Meta-analyses are increasingly popular. It is unknown whether this popularity is driven by specific countries and specific meta-analyses types. PubMed was used to identify meta-analyses since 1995 (last update 9/1/2012) and catalogue their types and country of origin. We focused more on meta-analyses from China (the current top producer of meta-analyses) versus the USA (top producer until recently). The annual number of meta-analyses from China increased 40-fold between 2003 and 2011 versus 2.4-fold for the USA. The growth of Chinese meta-analyses was driven by genetics (110-fold increase in 2011 versus 2003). The HuGE Navigator identified 612 meta-analyses of genetic association studies published in 2012 from China versus only 109 from the USA. We compared in-depth 50 genetic association meta-analyses from China versus 50 from USA in 2012. Meta-analyses from China almost always used only literature-based data (92%), and focused on one or two genes (94%) and variants (78%) identified with candidate gene approaches (88%), while many USA meta-analyses used genome-wide approaches and raw data. Both groups usually concluded favorably for the presence of genetic associations (80% versus 74%), but nominal significance (P<0.05) typically sufficed in the China group. Meta-analyses from China typically neglected genome-wide data, and often included candidate gene studies published in Chinese-language journals. Overall, there is an impressive rise of meta-analyses from China, particularly on genetic associations. Since most claimed candidate gene associations are likely false-positives, there is an urgent global need to incorporate genome-wide data and state-of-the art statistical inferences to avoid a flood of false-positive genetic meta-analyses.

Impact of the ADHD-susceptibility gene CDH13 on development and function of brain networks

Attention-deficit/hyperactivity disorder (ADHD) is a common, early onset and enduring neuropsychiatric disorder characterized by developmentally inappropriate inattention, hyperactivity, increased impulsivity and motivational/emotional dysregulation with similar prevalence rates throughout different cultural settings. Persistence of ADHD into adulthood is associated with considerable risk for co-morbidities such as depression and substance use disorder. Although the substantial heritability of ADHD is well documented the etiology is characterized by a complex coherence of genetic and environmental factors rendering identification of risk genes difficult. Genome-wide linkage as well as single nucleotide polymorphism (SNP) and copy-number variant (CNV) association scans recently allow to reliably define aetiopathogenesis-related genes. A considerable number of novel ADHD risk genes implicate biological processes involved in neurite outgrowth and axon guidance. Here, we focus on the gene encoding Cadherin-13 (CDH13), a cell adhesion molecule which was replicably associated with liability to ADHD and related neuropsychiatric conditions. Based on its unique expression pattern in the brain, we discuss the molecular structure and neuronal mechanisms of Cadherin-13 in relation to other cadherins and the cardiovascular system. An appraisal of various Cadherin-13-modulated signaling pathways impacting proliferation, migration and connectivity of specific neurons is also provided. Finally, we develop an integrative hypothesis of the mechanisms in which Cadherin-13 plays a central role in the regulation of brain network development, plasticity and function. The review concludes with emerging concepts about alterations in Cadherin-13 signaling contributing to the pathophysiology of neurodevelopmental disorders.

The power of meta-analysis in genome-wide association studies

Meta-analysis of multiple genome-wide association (GWA) studies has become common practice over the past few years. The main advantage of this technique is the maximization of power to detect subtle genetic effects for common traits. Moreover, one can use meta-analysis to probe and identify heterogeneity in the effect sizes across the combined studies. In this review, we systematically appraise and evaluate the characteristics of GWA meta-analyses with 10,000 or more subjects published up to June 2012. We provide an overview of the current landscape of variants discovered by GWA meta-analyses, and we discuss and assess with extrapolations from empirical data the value of larger meta-analyses for the discovery of additional genetic associations and new biology in the future. Finally, we discuss some emerging logistical and practical issues related to the conduct of meta-analysis of GWA studies.

Identification of antithrombin-modulating genes. Role of LARGE, a gene encoding a bifunctional glycosyltransferase, in the secretion of proteins?

The haemostatic relevance of antithrombin together with the low genetic variability of SERPINC1, and the high heritability of plasma levels encourage the search for modulating genes. We used a hypothesis-free approach to identify these genes, evaluating associations between plasma antithrombin and 307,984 polymorphisms in the GAIT study (352 individuals from 21 Spanish families). Despite no SNP reaching the genome wide significance threshold, we verified milder positive associations in 307 blood donors from a different cohort. This validation study suggested LARGE, a gene encoding a protein with xylosyltransferase and glucuronyltransferase activities that forms heparin-like linear polysaccharides, as a potential modulator of antithrombin based on the significant association of one SNPs, rs762057, with anti-FXa activity, particularly after adjustment for age, sex and SERPINC1 rs2227589 genotype, all factors influencing antithrombin levels (p = 0.02). Additional results sustained this association. LARGE silencing inHepG2 and HEK-EBNA cells did not affect SERPINC1 mRNA levels but significantly reduced the secretion of antithrombin with moderate intracellular retention. Milder effects were observed on α1-antitrypsin, prothrombin and transferrin. Our study suggests LARGE as the first known modifier of plasma antithrombin, and proposes a new role for LARGE in modulating extracellular secretion of certain glycoproteins.

A nonsynonymous variant of IL1A is associated with endometriosis in Japanese population

Our previous genome-wide association study has demonstrated that single-nucleotide polymorphisms (SNPs) located in intronic and downstream regions of IL1A (interleukin 1α) were associated with the risk of endometriosis. These SNPs on the genome-wide association study platform could be only surrogates for the true causal variant. Thus, we resequenced all the exons of IL1A in 377 patients with endometriosis and 457 healthy controls. We detected seven rare variants (minor allele frequency <0.01) and four common variants. All the rare variants were not associated with endometriosis. The four common variants (rs17561, rs1304037, rs2856836 and rs3783553) in IL1A were significantly associated with endometriosis (P=0.0024, 0.0024, 0.0014 and 0.0061, respectively). All the four SNPs were within a linkage disequilibrium block. Among them, only rs17561 was nonsynonymous (p.A114S), which has been reported to be associated with susceptibility to ovarian cancer. Taken together, we examined association between rs17561 and endometriosis in an independent validation data set (524 patients and 533 healthy controls) replicating significant association (P=4.0 × 10(-5); odds ratio (OR), 1.91; 95% confidence interval (CI), 1.41-2.61). Meta-analysis by combining results from the two stages strengthened the evidence of association (P=2.5 × 10(-7); OR, 1.90; 95% CI, 1.49-2.43). Our findings demonstrated that the nonsynonymous variant of IL1A might confer genetic susceptibility to endometriosis in Japanese population.

White matter integrity as an intermediate phenotype: exploratory genome-wide association analysis in individuals at high risk of bipolar disorder

White matter integrity, as measured using diffusion tensor imaging (DTI), is reduced in individuals with bipolar disorder (BD), their unaffected relatives and carriers of specific risk-alleles. Fractional anisotropy (FA), an index of white matter integrity, is highly heritable but the genetic architecture of this trait has received little investigation. In this study we performed a genome-wide association study with FA as quantitative phenotype, in unaffected relatives of patients with BD (N=70) and a matched control group (N=80). Amongst our top results were SNPs located in genes involved in cell adhesion, white matter development and neuronal plasticity. Pathway analysis of the top associated polymorphisms and genes confirmed the enrichment of processes relevant to BD and white matter development, including axon guidance, ErbB-signalling neurotrophin signalling, phosphatidylinositol signalling, and cell adhesion. The majority of genes implicated in these pathways were differentially associated with FA in individuals at high familial risk, suggesting interactions with genetic background or environmental factors secondary to familial risk for BD. Although the present findings require independent replication, the results encourage the use of global FA as a quantitative phenotype in future large-scale studies which may help to identify the biological processes underlying reduced FA in BD and other psychiatric disorders.

SNP model development for the prediction of eye colour in New Zealand

The ability to predict externally visible characteristics (EVCs) from DNA has appeal for use in forensic science, particularly where a forensic database match is not made and an eye witness account is unavailable. This technology has yet to be implemented in casework in New Zealand. The broad cultural diversity and likely population stratification within New Zealand dictates that any EVC predictions made using anonymous DNA must perform accurately in the absence of knowledge of the donor's ancestral background. Here we construct classification tree models with SNPs of known association with eye colour phenotypes in three categories, blue vs. non-blue, brown vs. non-brown and intermediate vs. non-intermediate. A set of nineteen SNPs from ten different known or suspected pigmentation genes were selected from the literature. A training dataset of 101 unrelated individuals from the New Zealand population and representing different ancestral backgrounds were used. We constructed four alternate models capable of predicting eye colour from the DNA genotypes of SNPs located within the HERC2, OCA2, TYR and SLC24A4 genes using probability calculation and classification trees. The final model selected for eye colour prediction exhibited high levels of accuracy for both blue (89%) and brown eye colour (94%). Models were further assessed with a test set of 25 'blind' samples where phenotype was unknown, with blue and brown eye colour predicted correctly where model thresholds were met. Classification trees offer an aesthetically simple and comprehendible model to predict blue and brown eye colour.

Rare variants in complex traits: novel identification strategies and the role of de novo mutations

Following the limited success of linkage and association studies aimed at identifying the genetic causes of common neurodevelopmental syndromes like autism and schizophrenia, complex traits such as these have recently been considered under the 'common disease-rare variant' hypothesis. Prior to this hypothesis, the study of candidate genes has enabled the discovery of rare variants in complex disorders, and in turn some of these variants have highlighted the genetic contribution of de novo variants. De novo variants belong to a subcategory of spontaneous rare variants that are largely associated with sporadic diseases, which include some complex psychiatric disorders where the affected individuals do not transmit the genetic defects they carry because of their reduced reproductive fitness. Interestingly, recent studies have demonstrated the rate of germline de novo mutations to be higher in individuals with complex psychiatric disorders by comparison to what is seen in unaffected control individuals; moreover, de novo mutations carried by affected individuals have generally been more deleterious than those observed in control individuals. Advanced sequencing technologies have recently enabled the undertaking of massive parallel sequencing projects that can cover the entire coding sequences (exome) or genome of several individuals at once. Such advances have thus fostered the emergence of novel genetic hypotheses and ideas to investigate disease-causative genetic variations. The genetic underpinnings of a number of sporadic complex diseases is now becoming partly explained and more major breakthroughs for complex traits genomics should be expected in the near future.

Comprehensive characterization of human genome variation by high coverage whole-genome sequencing of forty four Caucasians

Whole genome sequencing studies are essential to obtain a comprehensive understanding of the vast pattern of human genomic variations. Here we report the results of a high-coverage whole genome sequencing study for 44 unrelated healthy Caucasian adults, each sequenced to over 50-fold coverage (averaging 65.8×). We identified approximately 11 million single nucleotide polymorphisms (SNPs), 2.8 million short insertions and deletions, and over 500,000 block substitutions. We showed that, although previous studies, including the 1000 Genomes Project Phase 1 study, have catalogued the vast majority of common SNPs, many of the low-frequency and rare variants remain undiscovered. For instance, approximately 1.4 million SNPs and 1.3 million short indels that we found were novel to both the dbSNP and the 1000 Genomes Project Phase 1 data sets, and the majority of which (∼96%) have a minor allele frequency less than 5%. On average, each individual genome carried ∼3.3 million SNPs and ∼492,000 indels/block substitutions, including approximately 179 variants that were predicted to cause loss of function of the gene products. Moreover, each individual genome carried an average of 44 such loss-of-function variants in a homozygous state, which would completely "knock out" the corresponding genes. Across all the 44 genomes, a total of 182 genes were "knocked-out" in at least one individual genome, among which 46 genes were "knocked out" in over 30% of our samples, suggesting that a number of genes are commonly "knocked-out" in general populations. Gene ontology analysis suggested that these commonly "knocked-out" genes are enriched in biological process related to antigen processing and immune response. Our results contribute towards a comprehensive characterization of human genomic variation, especially for less-common and rare variants, and provide an invaluable resource for future genetic studies of human variation and diseases.

GIGI: an approach to effective imputation of dense genotypes on large pedigrees

Recent emergence of the common-disease-rare-variant hypothesis has renewed interest in the use of large pedigrees for identifying rare causal variants. Genotyping with modern sequencing platforms is increasingly common in the search for such variants but remains expensive and often is limited to only a few subjects per pedigree. In population-based samples, genotype imputation is widely used so that additional genotyping is not needed. We now introduce an analogous approach that enables computationally efficient imputation in large pedigrees. Our approach samples inheritance vectors (IVs) from a Markov Chain Monte Carlo sampler by conditioning on genotypes from a sparse set of framework markers. Missing genotypes are probabilistically inferred from these IVs along with observed dense genotypes that are available on a subset of subjects. We implemented our approach in the Genotype Imputation Given Inheritance (GIGI) program and evaluated the approach on both simulated and real large pedigrees. With a real pedigree, we also compared imputed results obtained from this approach with those from the population-based imputation program BEAGLE. We demonstrated that our pedigree-based approach imputes many alleles with high accuracy. It is much more accurate for calling rare alleles than is population-based imputation and does not require an outside reference sample. We also evaluated the effect of varying other parameters, including the marker type and density of the framework panel, threshold for calling genotypes, and population allele frequencies. By leveraging information from existing genotypes already assayed on large pedigrees, our approach can facilitate cost-effective use of sequence data in the pursuit of rare causal variants.